www.rsisinternational.org

Page 3067

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue VI, June 2026

Water Quality Monitoring for the Sustainable Management of

Aquatic Ecosystem in the Markandeya Dam Reservoir, Kolar

District, Karnataka, India

Gayathri S., Tejushree H. S., Yashaswini R., Shashikumar C., Shravani. K

Water Quality Research Unit, Department of Zoology, Bangalore University — JB Campus, Bengaluru

560056, Karnataka, India

DOI: https://doi.org/10.51583/IJLTEMAS.2026.150600224

Received: 12 July 2026; Accepted: 17 July 2026; Published: 25 July 2026

ABSTRACT

Water is an irreplaceable natural resource and the cornerstone of aquatic and terrestrial ecosystems alike. Dams

and reservoirs are critical infrastructure that support biodiversity, agriculture, potable water supply, hydropower

generation, flood mitigation, and recreation. Despite their ecological importance, reservoir water quality remains

vulnerable to seasonal fluctuations, anthropogenic pollution, and nutrient enrichment. The present study

evaluates the physico-chemical characteristics and plankton community structure of Markandeya Dam, Kolar

District, Karnataka, India, across pre-monsoon, monsoon, and post-monsoon seasons from February to July

2025. Phytoplankton (22 taxa) and zooplankton (14 taxa) were enumerated and subjected to nine diversity

indices. A Water Quality Index (WQI) computed via the Weighted Arithmetic Index method yielded a value of

46.57, classifying the reservoir as moderately polluted. Nygaard's Water Quality Indices (NWQI) further

indicated a mesotrophic trophic status. Most physico-chemical parameters conformed to WHO and BIS

drinking-water standards; however, dissolved oxygen variability and elevated faecal coliform counts signal

localized organic pollution and microbial contamination risks. Shannon–Wiener diversity indices for both

phytoplankton (H' = 0–1.45) and zooplankton (H' = 0–0.92) indicated moderate pollution, with dominance by

Chlorophyceae and Monogononta rotifers, respectively. Seasonal patterns revealed higher diversity and species

richness during cooler pre-monsoon months and greater dominance during warm transitional periods. The

integration of WQI, NWQI, and multi-index plankton assessment constitutes a robust and replicable framework

for the ecological monitoring and sustainable management of tropical reservoir ecosystems.

Keywords: Physico-chemical parameters; Water Quality Index; Nygaard's Water Quality Indices;

Phytoplankton diversity; Zooplankton community; Mesotrophic reservoir; Kolar District; Karnataka

INTRODUCTION

Water is fundamental to life, shaping public health, economic productivity, and the structural integrity of natural

ecosystems. Although approximately 71% of the Earth's surface is covered by water, only a minute fraction exists as

accessible freshwater, rendering inland water bodies of exceptional ecological and socio-economic significance (Miller &

Lake, 2014). Reservoirs and dam systems are engineered freshwater ecosystems that simultaneously serve as water-supply

infrastructure and as habitats for diverse aquatic communities. They regulate streamflow, recharge groundwater, support

irrigation, enable hydropower, and provide recreational and cultural services (Johnson & Wang, 2020).

Despite these multifaceted services, reservoirs are susceptible to a range of stressors that progressively degrade water

quality and ecological function. Anthropogenic pressures—including agricultural runoff carrying nutrients and pesticides,

untreated sewage discharge, land-use change in catchment areas, and increasing human water demand—exacerbate natural

seasonal fluctuations in temperature, precipitation, and nutrient dynamics (Chen et al., 2021; Smith et al., 2018). The

cumulative outcome is nutrient loading, eutrophication, dissolved-oxygen depletion, and microbial contamination, all of

which compromise the use of reservoir water for drinking, fisheries, and agriculture.

www.rsisinternational.org

Page 3068

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue VI, June 2026

Plankton communities are sensitive ecological indicators of water quality and trophic status. Phytoplankton—comprising

diatoms, green algae, cyanobacteria, and dinoflagellates—occupy the base of the aquatic food web and mediate oxygen

production and carbon cycling (Falkowski & Raven, 2007).

Zooplankton link primary producers to higher trophic levels and modulate phytoplankton biomass through grazing

pressure (Balseiro et al., 2023; Mohammed et al., 2023). The composition and diversity of both groups respond rapidly to

environmental perturbations, making them ideal biomonitors (Kour et al., 2022; Enawgaw et al., 2023).

The Water Quality Index (WQI) is a widely adopted aggregation tool that distils multiparametric physico-chemical data

into a single dimensionless score, facilitating public communication and decision-making (Horton, 1965).

Nygaard's Water Quality Indices (NWQI) complement WQI by providing a phytoplankton-based assessment of trophic

state (Hutchinson, 1967, after Nygaard, 1949). Together, these frameworks deliver a comprehensive picture of reservoir

health that neither chemical nor biological approaches achieve in isolation.

Regional studies have documented varied water quality conditions in South Indian reservoirs. Manjare et al. (2010)

characterised the physico-chemistry of Tamdalge tank in Maharashtra, while Basavaraj and Kadadevaru (2024) evaluated

physico-chemical parameters and zooplankton in Gopalaswamy tank, Chitradurga. Asulabha et al. (2022) catalogued

phytoplankton diversity across Bangalore lakes, identifying 58 genera.

Farnaz and Rahmatullah (2021) and Garg (2022) have similarly characterised water quality in Bihar and Haryana ponds,

respectively, using physico-chemical indices. However, Markandeya Dam—a historically significant irrigation and water-

supply reservoir in Kolar District, Karnataka—remains poorly characterised with respect to integrated biological and

chemical monitoring.

The present study therefore aims to: (i) characterise seasonal variation in physico-chemical water quality across pre-

monsoon, monsoon, and post-monsoon periods; (ii) assess phytoplankton and zooplankton community structure and

diversity; (iii) determine the Water Quality Index and Nygaard trophic classification; and (iv) identify monitoring and

management priorities for sustainable utilisation of the reservoir's water resources.

Description of the Study Area

Markandeya Dam is situated at geographic coordinates 16°2'0" N, 74°38'30" E, near Budikote village in Malur Taluk,

Kolar District, Karnataka, India (Fig. 1).

Constructed between 1936 and 1940, the dam impounds the Markandeya River, which bifurcates in the vicinity of the

structure. The crest elevation is 30 m, the dam wall extends 750 m in length, and the surrounding terrain lies at an average

elevation of approximately 794 m above mean sea level.

The reservoir has a storage capacity of 553.61 TMC ft and drains a catchment area of approximately 19.71 km². It was

constructed at a project cost of Rs. 43.20 crore with the primary objective of supplying potable and irrigation water to 184

villages in Bangarpet Taluk via a 14 km pipeline extending to Tekal village.

The region experiences a semi-arid tropical climate characterised by a hot dry season (March–May), a southwest monsoon

(June–September), and a mild post-monsoon winter (October–February), with mean annual rainfall of approximately 750

mm.

The dam's catchment is predominantly agricultural, with crops of ragi, groundnut, and vegetables. Human settlements,

livestock grazing, and the absence of a buffer zone around the reservoir margin render the water body vulnerable to non-

point-source pollution. The study was conducted at a single representative sampling station located near the dam wall at

a depth of 0.5–1.0 m, representing the principal water abstraction zone.

www.rsisinternational.org

Page 3069

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue VI, June 2026

Figure : 1 – Location map of Markandeya Dam.

MATERIALS AND METHODS

Sample Collection and Preservation — Physico-chemical Analysis

Water samples were collected monthly from February to July 2025, covering post-monsoon (February), early pre-monsoon

(March–May), onset of monsoon (June), and mid-monsoon (July) periods. Surface grab samples were collected at 0.5 m

depth between 08:00 and 09:00 h using acid-washed 1 L polyethylene containers. All containers were pre-rinsed three

times with sample water prior to collection. Samples were labelled with site, date, and time to prevent misidentification.

Temperature and pH were measured in situ using a calibrated mercury glass thermometer and a portable digital pH meter

(±0.01 unit resolution), respectively. Remaining samples were transported on ice to the laboratory and analysed within 24

h following the Standard Methods for the Examination of Water and Wastewater (APHA, 2005).

Parameters determined in the laboratory included total dissolved solids (TDS; gravimetric method), electrical conductivity

(EC; conductivity bridge), dissolved oxygen (DO; Winkler's iodometric titration), biochemical oxygen demand (BOD; 5-

day BOD test at 20°C), chemical oxygen demand (COD; dichromate reflux method), chloride (argentometric titration),

total alkalinity (titrimetric method), total hardness (EDTA complexometric titration), phosphate (molybdenum blue

spectrophotometric method), nitrate (UV spectrophotometry at 220 nm), sulphate (barium sulphate turbidimetric method),

and faecal coliform (membrane filtration technique). All reagents were of analytical grade.

Sample Collection and Preservation — Plankton Analysis

Plankton samples were collected concurrently with physico-chemical samples. Qualitative and quantitative phytoplankton

samples were collected using a 25 µm mesh plankton net via both vertical and horizontal hauls. For quantitative

enumeration, unconcentrated phytoplankton samples were collected from beneath the surface at 0.5 m depth, while

zooplankton samples were obtained at 1.0 m depth. All plankton samples were preserved immediately in Lugol's iodine

solution (1% final concentration). Plankton cells and organisms were counted under a compound microscope using a

Sedgwick-Rafter (S-R) counting cell. Identification to genus and species level was conducted following APHA (2005)

standard taxonomic keys.

www.rsisinternational.org

Page 3070

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue VI, June 2026

Statistical and Index Analyses

Physico-chemical data were analysed using SPSS v. 16.0 (SPSS Inc., Chicago, IL, USA). Pearson's correlation coefficients

(r) were computed to evaluate pairwise relationships among the 15 parameters. The Water Quality Index (WQI) was

determined following the Weighted Arithmetic Index (WAI) method of Horton (1965), comprising four steps: (i)

parameter selection, (ii) sub-index (Q-value) generation, (iii) weight factor (Wi) assignment, and (iv) aggregation to a

composite WQI score. The trophic status of the reservoir was determined using Nygaard's Composite Quotient and

associated algal indices (Hutchinson, 1967, after Nygaard, 1949).

Plankton diversity was characterised using nine indices computed in PAST v. 4.0 (Hammer et al., 2001): Shannon–Wiener

diversity index (H'), Simpson's diversity index (1 – D), Pielou's evenness (J'), Dominance index (D), Menhinick's richness

index, Margalef's richness index, Fisher's alpha index, and the Berger–Parker dominance index. Water quality

interpretation of H' values followed the classification of Wilhm and Dorris (1968): H'> 3 = clean water; 1 ≤ H' ≤ 3 =

moderate pollution; H'< 1 = heavy pollution.

RESULTS

Physico-chemical Parameters

Monthly variation in the 15 physico-chemical parameters measured from February to July 2025 is presented in Table 1.

Atmospheric temperature ranged from 28.0°C to 29.0°C (mean 28.73 ± 0.40°C), reflecting the consistently warm tropical

climate of the study region. Water temperature tracked atmospheric temperature closely, ranging from 26.0°C to 30.5°C

(mean 27.83 ± 1.91°C); peak values in April–May are consistent with pre-monsoon solar heating, while monsoon season

values (June–July) were moderated by cloud cover and rainfall. These temperature ranges are within the tolerance limits

of most tropical freshwater organisms.

Water pH fluctuated between 6.0 (May) and 8.0 (June) with a mean of 7.00 ± 0.70, spanning near-neutral to slightly

alkaline conditions. All values fell within the BIS permissible range (6.5–8.5) for drinking water, indicating adequate

buffering capacity. TDS ranged from 248 to 289 mg/L (mean 272.50 ± 16.40 mg/L), substantially below the BIS

acceptable limit of 500 mg/L, affirming low mineralisation and good palatability. Electrical conductivity (EC) ranged

from 370.14 to 431.34 µS/cm (mean 406.71 ± 24.49 µS/cm), commensurate with the TDS values and consistent with

freshwater systems of low ionic strength.

Dissolved oxygen (DO) exhibited the greatest variability of any parameter, ranging from 1.0 mg/L (July) to 10.4 mg/L

(May), with a mean of 4.06 ± 3.34 mg/L. Values below 4 mg/L, observed in February and July, represent hypoxic

conditions that can compromise aerobic aquatic life; the peak in May likely reflects intense algal photosynthesis during a

bloom event. BOD ranged from 1.0 to 4.4 mg/L (mean 2.46 ± 1.16 mg/L), indicating moderate biodegradable organic

loading. COD showed wider fluctuation (1.6–25.6 mg/L; mean 11.73 ± 8.56 mg/L), with the February peak (25.6 mg/L)

suggesting elevated non-biodegradable organic or chemical inputs during the post-monsoon residual period.

Chloride concentrations were low (48–60 mg/L; mean 55.16 ± 4.66 mg/L), well within the BIS limit of 250 mg/L, ruling

out significant saline or sewage influence on ion balance. Total alkalinity exhibited the highest coefficient of variation of

all parameters (range: 8–348 mg/L; mean 123.66 ± 122.66 mg/L), with the June peak (348 mg/L) likely reflecting

concentration of bicarbonates from catchment weathering following the onset of monsoon inflow. Total hardness ranged

from 28 to 204 mg/L (mean 76.33 ± 67.85 mg/L), indicative of soft to moderately hard water. Phosphate concentrations

remained low (0.0–1.0 mg/L; mean 0.41 ± 0.37 mg/L), as did nitrate (0–10 mg/L; mean 2.50 ± 4.18 mg/L)—both well

below WHO thresholds. Sulphate ranged from 8 to 26 mg/L (mean 19.00 ± 7.01 mg/L), far below the BIS acceptable limit

of 200 mg/L.

Faecal coliform counts ranged from 126 to 450 cfu/100 mL (mean 297.33 ± 106.48 cfu/100 mL). These levels substantially

exceed the WHO guideline of 0 cfu/100 mL for drinking water and 100 cfu/100 mL for recreational use, indicating

significant faecal contamination attributable to open defecation, livestock watering, and surface runoff from agricultural

fields in the catchment.

www.rsisinternational.org

Page 3071

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue VI, June 2026

Table : 1 - Monthly variation in physico-chemical parameters of Markandeya Dam, Kolar District, Karnataka (February–

July 2025).

Sl.

No.

Parameter

Feb

Mar

Apr

May

Jun

Jul

Max

Min

Mean ± SD

Atm. Temperature (°C)

28.5

29.0

28.9

28.0

29.0

28.0

28.73 ± 0.40

Water Temperature (°C)

26.0

26.5

30.0

30.5

27.0

30.5

26.0

27.83 ± 1.91

7.5

6.5

7.0

6.0

8.0

7.0

8.0

6.0

7.00 ± 0.70

TDS (mg/L)

286

278

289

277

248

257

289

248

272.50 ± 16.40

EC (µS/cm)

426.9

414.9

431.3

413.4

370.1

383.6

431.3

370.1

406.71 ± 24.49

DO (mg/L)

1.8

4.0

4.2

10.4

3.0

1.0

10.4

1.0

4.06 ± 3.34

BOD (mg/L)

2.6

2.4

1.0

4.4

2.8

1.6

4.4

1.0

2.46 ± 1.16

COD (mg/L)

25.6

9.6

1.6

6.4

17.6

25.6

1.6

11.73 ± 8.56

Chloride (mg/L)

55.16 ± 4.66

Total Alkalinity (mg/L)

106

348

164

348

123.66 ±

122.66

Total Hardness (mg/L)

102

204

76.33 ± 67.85

Phosphate (mg/L)

0.5

0.0

1.0

0.0

1.0

0.0

0.41 ± 0.37

Nitrate (mg/L)

0.0

10.0

0.0

5.0

0.0

10.0

0.0

2.50 ± 4.18

Sulphate (mg/L)

19.00 ± 7.01

Faecal Coliform

(cfu/100 mL)

450

320

260

342

126

286

450

126

297.33 ±

106.48

Biostatistical (Correlation) Analysis

Pearson's correlation matrix revealed several ecologically meaningful parameter relationships. A significant positive

correlation between water temperature and DO (r = 0.631) suggests that photosynthetic oxygen production by

phytoplankton, rather than physical dissolution, governs DO during warmer months. The positive pH–COD correlation (r

= 0.423) implies that organic matter decomposition under alkaline conditions drives chemical oxygen demand. The

positive COD–sulphate association (r = 0.426) may reflect anaerobic sulphate reduction mediated by organic substrates

during stratification.

Strong negative correlations were observed for pH–nitrate (r = −0.676) and pH–DO (r = −0.720), indicating that elevated

biological activity and carbon dioxide production during algal blooms can suppresses pH while concurrently depleting

dissolved oxygen. The positive DO–nitrate correlation (r = 0.444) is consistent with nitrification processes that occur

www.rsisinternational.org

Page 3072

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue VI, June 2026

under aerobic conditions. The total hardness–phosphate correlation (r = 0.588) likely reflects geochemical co-leaching of

calcium, magnesium, and phosphorus from catchment soils during high-intensity monsoon rainfall events.

Water Quality Index (WQI)

The WQI was computed for seven key parameters using the Weighted Arithmetic Index method; results are summarised

in Table 2. The water quality index (WQI) was calculated using weighted arithmetic index (WAI) approach (refer equation

1,2 and 3). The objective of water quality index was originally proposed by Horton (1965). A commonly-used water

quality index (WQI) was developed by the National Sanitation Foundation (NSF) in 1970 (Brown et al. 1970). Parameters

as dissolved oxygen, fecal coliforms, pH, biological oxygen demand, phosphate, nitrate and total dissolved solids were

recognized as preliminary indication of quality as is used in calculating quality index. In the present study the composite

WQI score of 46.57 classifies the reservoir as 'moderately polluted' water.

Table : 2 - Water Quality Index (WQI) determination for Markandeya Dam, Kolar District, Karnataka (Weighted

Arithmetic Index method; Horton, 1965).

Sl.

No.

Parameter

Unit

Estimated

Value (Mi)

Q-Value

(Qi)

Weight

Factor (Wi)

WiQi

WQI

mg/L

4.006

124.175

0.166

20.613

46.57

(Moderate)

Faecal Coliform

cfu/100

297.33

235.900

0.0079

1.864

—

7.000

0.000

0.105

0.000

BOD

mg/L

2.46

41.000

0.166

6.806

Phosphate

mg/L

0.41

8.200

0.200

1.640

Nitrate

mg/L

2.50

5.550

0.022

0.122

TDS

mg/L

272.5

54.500

0.002

0.109

∑Wi = 0.668 ∑WiQi = 31.154

= M

– I

X 100 (1)

- I

Where : Q

= quality rating corresponding to the i

parameter is a number reflecting the relative value of the

parameter, M

= estimated values of the parameters in the laboratory, I

= Ideal values of the i

parameter (ideal

values are taken as zero except for pH = 7 and DO =14) and S

= standard values of the i

parameter.

= k (2)

www.rsisinternational.org

Page 3073

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue VI, June 2026

Where : W

= unit weight ; k = 1 ; S

= recommended standards of the corresponding parameter .

WQI = ∑W

(3)

∑W

Where : WQI = the overall water quality index.

Table : 3 - Correlation of physico-chemical parameters in Markandeya Dam during Feb-July 2025

*Correlation is significant at the 0.05 level (2-tailed)

**Correlation is significant at the 0.01 level (2-tailed)

TDS

Chlorid

Phospha

Nitrate

Sulpha

.469

-.242

.334

.631

.323

-.701

.532

-.215

.323

.412

.468

-.070

-.220

-.554

.312

.777

.203

-.667

-.108

-.523

-.354

-.370

.000

-.328

-.117

-.431

-.720

-.365

.423

-.242

.749

.534

.751

-.676

.121

-.401

TDS

1.000

.268

-.092

.191

.736

-.885

-.675

-.219

.226

.391

.703

.268

-.092

.192

.736

-.885

-.675

-.219

.226

.391

.704

.723

-.755

.248

-.435

-.119

-.345

.444

-.591

.083

BOD

-.428

.270

.057

.211

-.167

.370

-.843

.182

COD

.117

.004

-.357

-.033

-.402

.426

.558

Chloride

-.472

-.110

.180

.538

.226

.610

.835

.588

-.341

-.291

-.646

.777

.074

-.226

-.709

Phosphat

-.159

.189

-.511

Nitrate

.102

.202

Sulphate

.108

www.rsisinternational.org

Page 3074

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue VI, June 2026

Table : 4 - The rating of the Trophic state Indices of Nygaard’s is presented below:

4.4 Phytoplankton Community and Diversity

A total of 22 phytoplankton taxa were identified from Markandeya Dam across the study eperiod. The taxonomic

composition was dominated by Chlorophyceae (10 taxa), followed by Cyanophyceae (3 taxa), Bacillariophyceae (2 taxa),

Zygnematophyceae (1 taxon ), Oligohymenophora (1 taxon), Tubulinea (1 taxon), Ulvophyceae (2 taxa), Dinophyceae (1

taxon), and Chrysophyceae (1 taxon). The percentage composition of the phytoplankton groups are shown in Figure : 2.

Phytoplankton diversity indices revealed pronounced seasonality. The Shannon–Wiener index (H') peaked at 1.45 in

February (moderate pollution) and fell to 0 in June (heavy pollution), indicating low diversity. The Dominance index

reached 1 in May, corresponding to a near-monoculture bloom, while the Berger–Parker index confirmed that a single

taxon constituted 50–100% of the community depending on season. Margalef's richness was highest in February (1.35)

and collapsed to 0 in May, consistent with bloom-induced competitive exclusion of minor taxa. Fisher's alpha was highest

in Feb (1.8). Collectively, the indices indicate pre-monsoon as the period of highest ecological resilience and monsoon-

onset as a period of ecological stress or bloom disturbance.

Asulabha et al. (2022) reported 58 phytoplankton genera across Bangalore lakes, indicating substantially higher regional

phytoplankton richness than observed in Markandeya Dam (22 taxa). The comparatively lower richness in the present

study may reflect the semi-arid location of Kolar District and the relatively shallow, fluctuating nature of the reservoir.

Table : 5 - Diversity index of Phytoplankton Group in Markandeya dam during Feb –July 2025

Feb

March

April

May

June

July

Taxa_S

Individuals

105

117

102

132

Dominance_D

0.3188

0.3968

0.3927

0.5002

0.5005

Shannon_H

1.456

1.152

1.125

0.7563

0.7773

Simpson_1-D

0.6812

0.6032

0.6073

0.4998

0.4995

Evenness_e^H/S

0.6124

0.5275

0.6159

0.7101

0.7252

Menhinick

0.7593

0.5855

0.4623

0.1213

0.297

0.2611

Margalef

1.351

1.074

0.84

0.4324

0.4096

Equitability_J

0.748

0.6431

0.6988

0.6884

0.7075

Fisher_alpha

1.808

1.381

1.061

0.1662

0.5796

0.5463

Berger-Parker

0.5059

0.5238

0.5128

0.5882

0.6061

www.rsisinternational.org

Page 3075

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue VI, June 2026

Zooplankton Community and Diversity

Fourteen zooplankton taxa were identified, with a community heavily dominated by Monogononta rotifers (7 taxa;

77% of total taxa), including Brachionus calyciflorus and Keratella cochlearis as codominant species. Additional

groups comprised Eurotatoria (3 taxa; 14%), Maxillopoda copepods (3 taxa; 5%), and Crustacea (1 taxon; 8%). The

predominance of hardy, pollution-tolerant rotifers is characteristic of eutrophic or anthropogenically stressed

freshwater systems in which rapid rotifer reproduction confers a competitive advantage over more sensitive

crustacean zooplankton (Kour et al., 2022; Enawgaw et al., 2023).

Zooplankton Shannon–Wiener diversity was consistently low (H' = 0.25–0.92), peaking in April (H' = 0.92) and

falling to its lowest in May (H' = 0.25). Dominance was complete in February and March (Dominance index = 1.0;

Berger–Parker index = 1.0), consistent with single-species rotifer dominance during cooler, drier conditions. The

Margalef index increased from 0 in February–March to 0.70 in July, suggesting monsoon-driven recruitment of taxa

from inflowing streams. These patterns parallel those of phytoplankton and reinforce the narrative of seasonal

community restructuring driven by temperature, hydrology, and trophic dynamics.

Numerous investigations have demonstrated the utility of zooplankton as ecological and water quality indicators in

tropical reservoirs (Imoobe & Akoma, 2008; Beyene et al., 2022; Ginatullina et al., 2023; Oh et al., 2023; Ndah et

al., 2022; Rashid & Prakash, 2022). The present findings corroborate this body of evidence: low zooplankton

diversity, rotifer dominance, and depressed crustacean presence collectively signal moderate ecological stress and

water quality degradation in Markandeya Dam.

Feb

March

April

May

June

July

Taxa_S

Individuals

Dominance_D

0.455

0.8673

0.5026

0.5751

Shannon_H

0.9291

0.2573

0.8596

0.8449

Simpson_1-D

0.545

0.1327

0.4974

0.4249

Evenness_e^H/S

0.8441

0.6467

0.7874

0.5819

51%

25%

12%

Figure : 2 - Percentage composition of Phytoplankton in

Markandeya Dam during Feb-July 2025

Chlorophyceae

Cyanophyceae

Bacillariophyceae

Zygnematophyceae

Oligohymenophorea

Tubulinea

Ulvophyceae

Dinophyceae

Chrysophyceae

www.rsisinternational.org

Page 3076

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue VI, June 2026

Menhinick

0.7071

0.3162

0.5145

0.378

0.3612

0.4781

Margalef

0.5672

0.3001

0.4724

0.7061

Equitability_J

0.8457

0.3712

0.7825

0.6095

Fisher_alpha

0.7959

0.2766

0.7935

0.493

0.6396

0.9208

Berger-Parker

0.6176

0.9286

0.6667

0.7429

Table : 6 - Diversity indices of Zooplankton group in Markandeya Dam during Feb-July 2025

DISCUSSION

The physico-chemical profile of Markandeya Dam broadly conforms to acceptable standards for freshwater reservoirs

in tropical India, with most parameters within BIS and WHO limits. The reservoir can be characterised as a low-

mineralisation, near-neutral to slightly alkaline system with moderate organic loading—a profile consistent with its

semi-arid, largely agricultural catchment. However, several parameters warrant management attention.

Dissolved oxygen variability (1.0–10.4 mg/L) is the most ecologically critical finding from the physico-chemical

survey. Hypoxic conditions (<4 mg/L) in February and July likely reflect decomposition of accumulated organic

matter during post-monsoon stagnation and monsoon-onset stratification, respectively. Such conditions can trigger

fish kills, promote anaerobic decomposition pathways, and release sediment-bound phosphorus, potentially triggering

internal nutrient loading and accelerating eutrophication—a feedback loop widely documented in shallow tropical

reservoirs (Johnson & Wang, 2020).

Faecal coliform counts exceeding 126–450 cfu/100 mL represent the most acute water quality concern for human

health. These values indicate persistent faecal contamination exceeding WHO recreational and drinking-water

guidelines by 1–2 orders of magnitude. The contamination pathway is most plausibly diffuse: open defecation and

livestock access to the reservoir margin, combined with surface runoff from periurban agricultural areas. Without

source-control interventions, the dam water requires multi-barrier treatment (coagulation, filtration, and chlorination)

prior to domestic use.

Figure : 3 - Percentage composition of Zooplankton

In Markandeya Dam during Feb-July s2025

Monogononta

Euratatoria

14%

Crustacea

Maxillopoda

74%

www.rsisinternational.org

Page 3077

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue VI, June 2026

The WQI value of 46.57 is situated in the 'moderately polluted' band (26–50), confirming that the reservoir is not

pristine but retains ecological and utilitarian value. Nygaard's trophic indices, corroborated by Chlorophyceae

dominance and cyanobacterial co-occurrence in phytoplankton, consistently classify the reservoir as mesotrophic.

Mesotrophy represents a transitional trophic state that, under continued nutrient loading, may shift toward eutrophy—

a trajectory observed in many peri-urban South Indian reservoirs (Asulabha et al., 2022). The integrated WQI–NWQI

framework provides a more complete assessment than either approach in isolation, as WQI captures chemical hazards

and NWQI reflects biological nutrient status.Bloom-forming cyanobacteria produce cyanotoxins that are hazardous

to livestock, fish, and humans and are resistant to conventional water treatment (Falkowski & Raven, 2007).

Zooplankton rotifer dominance and suppression of cladocerans and copepods—the primary phytoplankton grazers—

signal a trophic cascade that may reduce top-down control of algal biomass. In less-disturbed oligotrophic to

mesotrophic systems, cladocerans such as Daphnia provide substantial phytoplankton grazing pressure; their absence

or scarcity in Markandeya Dam may allow phytoplankton biomass to accumulate unchecked during nutrient-rich

monsoon periods. Restoring zooplankton community structure through reduction of anthropogenic nutrient loading

could therefore yield co-benefits for algal control.

The seasonal pattern of increasing diversity in February (pre-monsoon winter) and declining diversity in May–June

(hot season and monsoon onset) is consistent with the intermediate disturbance hypothesis, under which intermediate

levels of physical disturbance (thermal mixing in winter) promote coexistence, while thermal stratification and bloom

events suppress it. This pattern has ecological management implications: pre-monsoon monitoring windows capture

maximum biodiversity and therefore offer the most sensitive baseline for detecting chronic pollution impacts.

CONCLUSION

This study provides the first comprehensive integrated assessment of water quality and plankton biodiversity in

Markandeya Dam, Kolar District, Karnataka. The principal conclusions are as follows:

(1) Most physico-chemical parameters conform to BIS and WHO standards, supporting the reservoir's potential for

irrigation and treated drinking water supply; however, dissolved oxygen variability and elevated faecal coliform

counts (297.33 ± 106.48 cfu/100 mL) indicate persistent organic pollution and microbial contamination that require

immediate remediation.

(2) A composite WQI of 46.57 classifies Markandeya Dam as moderately polluted water. Nygaard's trophic

assessment categorises the reservoir as mesotrophic, with a risk of eutrophication progression under continued

anthropogenic pressure.

(3) Twenty-two phytoplankton and 14 zooplankton taxa were recorded. Shannon diversity indices (H' = 0–1.45 and

H' = 0–0.92, respectively) and taxonomic composition confirm moderate pollution conditions, with Chlorophyceae

and Monogononta rotifers as diagnostic dominants.

(4) Seasonal diversity maxima in February and minima in May–June reflect the interplay of temperature, thermal

stratification, and bloom dynamics, with pre-monsoon periods providing the most sensitive monitoring window.

(5) The concurrent application of WQI, NWQI, and multi-index plankton biodiversity assessment constitutes a

replicable, cost-effective framework for the ecological surveillance and sustainable management of tropical dam

reservoirs.

Management recommendations include: installation of riparian buffer strips to intercept agricultural runoff;

prohibition of livestock access to the reservoir margin; construction of community sanitation facilities in surrounding

villages; regular cyanotoxin monitoring during pre-monsoon bloom periods; and long-term quarterly monitoring to

detect trophic trajectory shifts. Future investigations should extend the sampling period to a full hydrological year,

www.rsisinternational.org

Page 3078

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue VI, June 2026

incorporate sediment nutrient analysis, and employ molecular (metabarcoding) approaches to enhance taxonomic

resolution of the plankton community.

Declarations

Funding

This research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Conflicts of Interest

The authors declare no conflicts of interest.

Author Contributions

Gayathri S.: Conceptualisation, Methodology, Formal Analysis, Writing — Original Draft. Tejushree H. S.: Sample

Collection, Laboratory Analysis. Yashaswini R.: Data Curation, Validation. Shashikumar C.: Supervision, Shravani.

K. Writing — Review and Editing.

Data Availability Statement

The datasets generated and analysed during the current study are available from the corresponding author upon

reasonable request.

Ethical Approval

This study involved water and plankton sample collection from a public reservoir and did not involve vertebrate

animals requiring ethical committee approval. Field sampling was conducted in compliance with local environmental

regulations.

REFERENCES

1. APHA (2005). Standard Methods for the Examination of Water and Wastewater (21st ed.).

American Public Health Association, Washington, DC.

2. Asulabha, K. S., Jaishanker, R., Sincy, V., & Ramachandra, T. V. (2022). Diversity of

phytoplankton in lakes of Bangalore, Karnataka, India. In Biodiversity Challenges: A Way

Forward (pp. 147–178). Springer, New Delhi.

3. Balseiro, E., Modenutti, B., Gutierrez, M. A., Sagarario, M. A. G., & Laspoumaderes, C. (2023).

Status of zooplankton ecology in freshwater ecosystems from Argentina. Limnologica, 100,

126011. https://doi.org/10.1016/j.limno.2022.126011

4. Basavaraj, S. K., & Kadadevaru, G. G. (2024). Assessment of physicochemical parameters and

zooplankton community at Gopalaswamy tank, Chitradurga, Karnataka. Indian Journal of Science

and Technology, 17(4), 368–372.

5. Beyene, G., Kifle, D., & Fetahi, T. (2022). Spatial distribution of zooplankton in relation to

selected physico-chemical parameters of Lake Hawassa, Ethiopia. African Journal of Aquatic

Science, 47(2), 163–172. https://doi.org/10.2989/16085914.2021.2003746

6. Brown RM, McClelland NI, Deininger RA, Tozer RG (1970) A water quality index: do we dare?

Water & Sewage Works,117(10):339-343.

7. Bureau of Indian Standards (BIS 2003)

8. Chen, X., Zhang, Y., & Li, Q. (2021). Lakes as indicators of environmental change: Impacts of

pollution, land-use change, and climate variability. Global Environmental Change, 70, 102345.

https://doi.org/10.1016/j.gloenvcha.2021.102345

www.rsisinternational.org

Page 3079

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue VI, June 2026

9. Enawgaw, Y., Wagaw, S., Wosnie, A., & Fetahi, T. (2023). Zooplankton as ecosystem indicators

and their effects on eutrophication in Lake Arekit, Ethiopia. Journal of Freshwater Ecology, 38(1),

2287433. https://doi.org/10.1080/02705060.2023.2287433

10. Falkowski, P. G., & Raven, J. A. (2007). Aquatic Photosynthesis (2nd ed.). Princeton University

Press, Princeton, NJ.

11. Farnaz, S., & Rahmatullah, M. (2021). Study of water quality using physico-chemical parameters

of two perennial ponds of Darbhanga District, Bihar. International Journal of Fisheries and

Aquatic Studies, 9(5), 95–98.

12. Garg, A. (2022). Physico-chemical parameters and planktons of water samples from Taraori pond

and Karna Lake, Karnal, Haryana. International Journal of Research in Engineering Sciences,

10(9), 456–464.

13. Ginatullina, E. N., Kurbanov, A. R., & Tuychiev, K. S. (2023). Influence of environmental factors

on zooplankton communities in a large lake system in Uzbekistan. E3S Web of Conferences, 407,

01004. https://doi.org/10.1051/e3sconf/202340701004

14. Hammer, Ø., Harper, D. A., & Ryan, P. D. (2001). PAST: Paleontological statistics software

package for education and data analysis. Palaeontologia Electronica, 4(1), 1–9.

15. Horton, R. K. (1965). An index-number system for rating water quality. Journal of the Water

Pollution Control Federation, 37(3), 300–305.

16. Hutchinson, G. E. (1967). A Treatise on Limnology, Vol. 2: Introduction to Lake Biology and the

Limnoplankton. John Wiley & Sons, New York.

17. Imoobe, T. O. T., & Akoma, A. O. (2008). Assessment of zooplankton community structure of

the Bahir Dar gulf of Lake Tana, Ethiopia. Ethiopian Journal of Environmental Studies and

Management, 1(2), 26–34. https://doi.org/10.4314/ejesm.v1i2.41577

18. Johnson, K. E., & Wang, H. (2020). Ecological dynamics of lakes: Nutrient loading,

sedimentation, and climatic influences. Limnology and Oceanography, 65(7), 1502–1517.

https://doi.org/10.1002/lno.11432

19. Kour, S., Slathia, D., Sharma, N., Kour, S., & Verma, R. (2022). Zooplankton as bio-indicators of

trophic status of a lentic water source, Jammu. Proceedings of the National Academy of Sciences,

India Section B: Biological Sciences, 92, 393–404. https://doi.org/10.1007/s40011-022-01349-z

20. Manjare, S. A., Vhanalakar, S. A., & Muley, D. V. (2010). Analysis of water quality using

physico-chemical parameters of Tamdalge tank in Kolhapur District, Maharashtra. International

Journal of Advanced Biotechnology Research, 1(2), 115–119.

21. Miller, J. R., & Lake, P. S. (2014). Lakes and their role in regional hydrology and ecology.

Environmental Reviews, 22(3), 245–260. https://doi.org/10.1139/er-2014-0023

22. Mohammed, A., Mengistou, S., & Fetahi, T. (2023). Effects of water quality parameters, water

level changes, and mixing on zooplankton community dynamics in tropical Lake Ardibo, Ethiopia.

Environmental Monitoring and Assessment, 195, 927. https://doi.org/10.1007/s10661-023-11500-

23. Ndah, A. B., et al. (2022). Zooplankton diversity and its relationship with water quality indicators.

Freshwater Biology, 67(4), 712–729.

24. Nygaard, G. (1949). Hydrobiological studies on some Danish ponds and lakes. Kongelige Danske

Videnskabernes Selskabs Biologiske Skrifter, 7(1), 1–293.

25. Oh, W. S., Park, G. C., Choi, J. H., Lee, H. B., & Lee, K. (2023). Density estimation of euphausiids

and copepods by multi-frequency method. Fisheries and Aquatic Sciences, 26(12), 689–697.

https://doi.org/10.47853/FAS.2023.e61

26. Rashid, H., & Prakash, A. (2022). Zooplankton community structure as water quality indicators

in freshwater bodies of Jammu, India. Journal of Limnology, 81(2), 45–58.

27. Smith, J. A., Brown, L. M., & Taylor, K. (2018). Freshwater biodiversity: Conservation status and

threats to lakes. Aquatic Conservation: Marine and Freshwater Ecosystems, 28(5), 1112–1127.

www.rsisinternational.org

Page 3080

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue VI, June 2026

28. Wilhm, J. L., & Dorris, T. C. (1968). Biological parameters for water quality criteria. BioScience,

18(6), 477–481.

29. World Health Organization (WHO 2005)